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友链</span></a></div></div><div id="toggle-menu"><a class="site-page"><i class="fas fa-bars fa-fw"></i></a></div></div></nav></header><main class="layout" id="content-inner"><div id="post"><div id="post-info"><h1 class="post-title">python模型建立</h1><div id="post-meta"><div class="meta-firstline"><span class="post-meta-date"><i class="far fa-calendar-alt fa-fw post-meta-icon"></i><span class="post-meta-label">发表于</span><time class="post-meta-date-created" datetime="2022-08-07T14:05:23.356Z" title="发表于 2022-08-07 22:05:23">2022-08-07</time><span class="post-meta-separator">|</span><i class="fas fa-history fa-fw post-meta-icon"></i><span class="post-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2022-08-18T11:09:33.491Z" title="更新于 2022-08-18 19:09:33">2022-08-18</time></span><span class="post-meta-categories"><span class="post-meta-separator">|</span><i class="fas fa-inbox fa-fw post-meta-icon"></i><a class="post-meta-categories" href="/categories/%E5%BB%BA%E6%A8%A1/">建模</a></span></div><div class="meta-secondline"><span class="post-meta-separator">|</span><span class="post-meta-wordcount"><i class="far fa-file-word fa-fw post-meta-icon"></i><span class="post-meta-label">字数总计:</span><span class="word-count">250</span><span class="post-meta-separator">|</span><i class="far fa-clock fa-fw post-meta-icon"></i><span class="post-meta-label">阅读时长:</span><span>1分钟</span></span><span class="post-meta-separator">|</span><span class="post-meta-pv-cv" id="" data-flag-title="python模型建立"><i class="far fa-eye fa-fw post-meta-icon"></i><span class="post-meta-label">阅读量:</span><span id="busuanzi_value_page_pv"><i class="fa-solid fa-spinner fa-spin"></i></span></span></div></div></div><article class="post-content" id="article-container"><blockquote>
<p>AI&#x2014;&#x623F;&#x4EF7;&#x9884;&#x6D4B;<br>@author: zqy</p>
</blockquote>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">from</span> matplotlib <span class="keyword">import</span> colors</span><br><span class="line">&lt;!-- <span class="comment">#&#x5BFC;&#x5165;&#x4F5C;&#x56FE;&#x5E93;&#x51FD;&#x6570; --&gt;</span></span><br><span class="line"><span class="keyword">import</span> matplotlib.pyplot <span class="keyword">as</span> plt </span><br><span class="line">&lt;!-- <span class="comment">#&#x5BFC;&#x5165;&#x6570;&#x7EC4;&#x5904;&#x7406;&#x5E93;&#x51FD;&#x6570; --&gt;</span></span><br><span class="line"><span class="keyword">import</span> numpy <span class="keyword">as</span> np </span><br><span class="line">&lt;!-- <span class="comment">#&#x5BFC;&#x5165;&#x673A;&#x5668;&#x5B66;&#x4E60;&#x5E93;&#x51FD;&#x6570;--&#x7EBF;&#x6027;&#x89C4;&#x5212;&#x6A21;&#x578B; --&gt;</span></span><br><span class="line"><span class="keyword">from</span> sklearn <span class="keyword">import</span> linear_model </span><br><span class="line">&lt;!-- <span class="comment">#&#x5BFC;&#x5165;&#x591A;&#x9879;&#x5F0F;&#x7279;&#x5F81;&#x8BA1;&#x7B97;&#x5E93;&#x51FD;&#x6570; --&gt;</span></span><br><span class="line"><span class="keyword">from</span> sklearn.preprocessing <span class="keyword">import</span> PolynomialFeatures </span><br><span class="line">&lt;!-- <span class="comment">#&#x8BFB;&#x53D6;&#x6570;&#x636E; --&gt;</span></span><br><span class="line">datasets_X=[<span class="number">150</span>,<span class="number">200</span>,<span class="number">250</span>,<span class="number">300</span>,<span class="number">350</span>,<span class="number">400</span>,<span class="number">600</span>]</span><br><span class="line">datasets_Y=[<span class="number">6450</span>,<span class="number">7450</span>,<span class="number">8450</span>,<span class="number">9450</span>,<span class="number">11450</span>,<span class="number">15450</span>,<span class="number">18450</span>]</span><br><span class="line">length=<span class="built_in">len</span>(datasets_X)</span><br><span class="line">&lt;!-- <span class="comment">#&#x5C06;&#x6570;&#x636E;&#x8F6C;&#x4E3A;&#x4E8C;&#x7EF4;&#x6570;&#x7EC4; --&gt;</span></span><br><span class="line">datasets_X=np.array(datasets_X).reshape([length,<span class="number">1</span>])</span><br><span class="line">datasets_Y=np.array(datasets_Y)</span><br><span class="line">&lt;!-- &#x7F16;&#x8F91;&#x9884;&#x5904;&#x7406;&#xFF0C;&#x6570;&#x636E;&#x5F52;&#x4E00;&#x5316;&#x5904;&#x7406; --&gt;</span><br><span class="line">minX=<span class="built_in">min</span>(datasets_X)</span><br><span class="line">maxX=<span class="built_in">max</span>(datasets_X)</span><br><span class="line">X=np.arange(minX,maxX).reshape([-<span class="number">1</span>,<span class="number">1</span>])</span><br><span class="line">&lt;!-- <span class="comment">#&#x5EFA;&#x7ACB;&#x591A;&#x9879;&#x5F0F;&#x56DE;&#x5F52;&#x6A21;&#x578B; --&gt;</span></span><br><span class="line">&lt;!-- <span class="comment">#&#x4F7F;&#x7528;&#x4E8C;&#x9636;&#x591A;&#x9879;&#x5F0F;&#x56DE;&#x5F52;&#x6A21;&#x578B; --&gt;</span></span><br><span class="line">poly_reg=PolynomialFeatures(degree=<span class="number">2</span>) </span><br><span class="line">X_poly=poly_reg.fit_transform(datasets_X)</span><br><span class="line">&lt;!-- <span class="comment">#&#x5EFA;&#x7ACB;&#x56DE;&#x5F52;&#x6A21;&#x578B; --&gt;</span></span><br><span class="line">lin_reg=linear_model.LinearRegression()  </span><br><span class="line">&lt;!-- <span class="comment">#&#x6A21;&#x578B;&#x8BAD;&#x7EC3; --&gt;</span></span><br><span class="line">lin_reg.fit(X_poly,datasets_Y)   </span><br><span class="line"></span><br><span class="line">&lt;!-- <span class="comment">#&#x663E;&#x793A;&#x6A21;&#x578B;&#x7ED3;&#x679C; --&gt;</span></span><br><span class="line">plt.scatter(datasets_X,datasets_Y,color=<span class="string">&apos;red&apos;</span>)</span><br><span class="line">plt.plot(X,lin_reg.predict(poly_reg.fit_transform(X)),color=<span class="string">&apos;blue&apos;</span>)</span><br><span class="line">plt.xlabel(<span class="string">&apos;Area&apos;</span>)</span><br><span class="line">plt.ylabel(<span class="string">&apos;Price&apos;</span>)</span><br><span class="line">plt.show()</span><br><span class="line"></span><br></pre></td></tr></table></figure>
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